When to make recognition of an object from the image, it is necessary to minimize the influence caused by illumination variation which would decrease success rate of the recognition. For this purpose, in WANG (Haitao Wang, Stan Z. Li, Yangsheng Wang, Generalized Quotient Image, Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, (2004) 498-505), there is proposed a method in which in face image recognition, a filter processing called “Self Quotient Image” is applied to an input image, so that the intrinsic feature robust against varying illumination is extracted, and the object recognition is performed.
The “Self Quotient Image” is a method in which “Center/Surround Retinex” of RAHMAN (METHOD OF IMPROVING A DIGITAL IMAGE, U.S. Pat. No. 5,991,456) is extended. An output image R(x, y) of the Center/Surround Retinex to an input image I(x, y) is defined by expression (1).
                              R          ⁡                      (                          x              ,              y                        )                          =                              I            ⁡                          (                              x                ,                y                            )                                                          G              ⁡                              (                                  x                  ,                  y                                )                                      *                          I              ⁡                              (                                  x                  ,                  y                                )                                                                        (        1        )            
Where, G(x, y) denotes a Gaussian function, and “*” denotes convolution. In RAHMAN, a Gaussian filter is applied to an input image to generate a smoothed image. In WANG, a weighted Gaussian filter is applied to an input image to generate a smoothed image.
There is a merit that the intrinsic feature robust against varying illumination can be calculated from only one given image, and the method is especially effective in the case where only one registration image can be obtained.
In the Center/Surround Retinex, as described in RAHMAN, when a normal direction and a lighting condition are constant in a local area of an input image, invariance to a diffuse reflection can be obtained. Diffuse reflection is one of the components caused by varying illumination.
However, in the case where the lighting condition varies in the local area, mainly in the case where a shadow is formed, the invariance can not be obtained, and erroneous recognition or identification is caused. The shadow area is formed on an object when light beams from a first light source are blocked by another object. Because the shadow area is illuminated by second light source (for example, environmental light) other than the first light source, the lighting condition in the shadow area is different from that in other areas.
FIG. 8 shows an example in which the Center/Surround Retinex is applied to a face image.
In an image 502, the influence caused by illumination is suppressed. The image 502 is generated from a face image 501 in which a shadow is formed on a person's face in an area on right-hand side and under a nose. However, since the lighting conditions in the shadow areas are different from those in other areas, it is impossible to completely remove the influence caused by illumination variations, as schematically indicated in the face image 502.
In order to prevent halo effects around edge, in the Self Quotient Image, when a smoothed image is generated, a weighted Gaussian filter (a weight function having anisotropic waveform is applied to a Gaussian function having isotropic waveform) is used. The weight function divides a local area into two sub-areas using the binarization.
However, in the binarization, there is a possibility that an area with a low albedo (for example, in the case of the face image recognition, a pupil or an eyebrow) is also erroneously detected as a shadow area. The important information representing the feature of the object would be also removed.
Besides, in an area where a pixel value is gradually changed like a soft shadow formed in a contour portion of a shadow area, since it is difficult to set a suitable threshold, an irregular or improper recognition would occur in the binarization technique.
Further, in an area where for example, specular reflection occurs and a reflection component varies, since it is difficult to extract a value of a diffuse reflection component from only the one given input image, the invariance can not be calculated in the conventional method.